Weather Forecasting Error in Solar Energy Forecasting
This work addresses the problem of improving solar energy forecasting accuracy for power grid operators, but it is incremental as it focuses on analyzing existing weather data errors rather than introducing new methods.
The study quantified the uncertainty in weather forecasting for solar PV energy forecasting by comparing six-day-ahead forecasted weather data with observed data using statistical metrics, identifying the most influential weather predictors and assessing their impact on forecasting performance.
As renewable distributed energy resources (DERs) penetrate the power grid at an accelerating speed, it is essential for operators to have accurate solar photovoltaic (PV) energy forecasting for efficient operations and planning. Generally, observed weather data are applied in the solar PV generation forecasting model while in practice the energy forecasting is based on forecasted weather data. In this paper, a study on the uncertainty in weather forecasting for the most commonly used weather variables is presented. The forecasted weather data for six days ahead is compared with the observed data and the results of analysis are quantified by statistical metrics. In addition, the most influential weather predictors in energy forecasting model are selected. The performance of historical and observed weather data errors is assessed using a solar PV generation forecasting model. Finally, a sensitivity test is performed to identify the influential weather variables whose accurate values can significantly improve the results of energy forecasting.